scholarly journals Sales plan generation problem on TV broadcasting

2016 ◽  
Vol 6 (2) ◽  
pp. 167-178
Author(s):  
Özlem Cosgun ◽  
İlkay Gultas
Keyword(s):  
Optimization Problem ◽  
Probability Distributions ◽  
Academic Research ◽  
Product Category ◽  
Demographic Information ◽  
Robust Solution ◽  
Plan Generation ◽  
Goal Program ◽  
Generation Problem ◽  
Tv Broadcasting

Major advertisers and/or advertisement agencies purchase hundreds of slots during a given broadcast period. Deterministic optimization approaches have been well developed for the problem of meeting client requests. The challenging task for the academic research currently is to address optimization problem under uncertainty. This paper is concerned with the sales plan generation problem when the audience levels of advertisement slots are random variables with known probability distributions. There are several constraints the TV networks must meet including client budget, product category and demographic information, plan weighting by week, program mix requirements, and the lengths of advertisement slots desired by the client. We formulate the problem as a chance constrained goal program and we demonstrate that it provides a robust solution with a user specified level of reliability.

scholarly journals Animated Two-Dimensional Barcode Generation Using Optimization Algorithms – Redesign of Formulation, Operator, and Quality Evaluation

2009 ◽  
Vol 13 (3) ◽  
pp. 245-254 ◽  
Author(s):  
Satoshi Ono ◽  
◽  
Kensuke Morinaga ◽  
Shigeru Nakayama
Keyword(s):  
Code Generation ◽  
Optimization Problem ◽  
Quality Evaluation ◽  
Qr Code ◽  
Two Dimensional ◽  
Quick Response ◽  
Qr Codes ◽  
Running Head ◽  
Generation Problem ◽  
Font Color

To improve on our previously proposed but problem-plagued innovation for generating animated and illustrated Quick Response (QR) codes, this paper proposes a method which formulates the animated QR code generation problem as an optimization problem rather than as a set of still QR code decoration problems. The proposed method also uses optimization operators designed for this problem and quality evaluation to maintain natural, smooth movement. Experiments demonstrate that the proposed method can generate animated QR codes involve a maximum of eight illustrations moving inside the code which maintaining decoding feasibility and smooth illustration movement.<FONT color="red" size="3">Erratum<br /></FONT> <FONT color="red" size="2">Due to a wrong manipulation during the correction of the proofs of the above paper, the running head title (short title) was incorrect. The correct running head title should have read as "Animated Two–Dimensional Barcode Generation."</FONT>

scholarly journals Duality Theorem for a Minimax Vector Optimization Problem

2021 ◽  
Author(s):  
Jacob Atticus Armstrong Goodall
Keyword(s):  
Vector Optimization ◽  
Duality Theorem ◽  
Optimization Problem ◽  
Optimal Transport ◽  
Probability Distributions ◽  
Vector Optimization Problem ◽  
Complexity Science ◽  
Polish Spaces ◽  
Leibler Divergence ◽  
Mathematical Optimisation

Abstract A duality theorem is stated and proved for a minimax vector optimization problem where the vectors are elements of the set of products of compact Polish spaces. A special case of this theorem is derived to show that two metrics on the space of probability distributions on countable products of Polish spaces are identical. The appendix includes a proof that, under the appropriate conditions, the function studied in the optimisation problem is indeed a metric. The optimisation problem is comparable to multi-commodity optimal transport where there is dependence between commodities. This paper builds on the work of R.S. MacKay who introduced the metrics in the context of complexity science in [4] and [5]. The metrics have the advantage of measuring distance uniformly over the whole network while other metrics on probability distributions fail to do so (e.g total variation, Kullback–Leibler divergence, see [5]). This opens up the potential of mathematical optimisation in the setting of complexity science.

Multi-Objective Robust Optimization Under Interval Uncertainty Using Online Approximation and Constraint Cuts

2011 ◽  
Vol 133 (6) ◽  
Author(s):  
W. Hu ◽  
M. Li ◽  
S. Azarm ◽  
A. Almansoori
Keyword(s):  
Optimization Problem ◽  
Optimization Problems ◽  
Computational Effort ◽  
Interval Uncertainty ◽  
Test Results ◽  
Robust Solution ◽  
Multi Objective ◽  
Function Calls ◽  
Level Problem ◽  
Upper Level

Many engineering optimization problems are multi-objective, constrained and have uncertainty in their inputs. For such problems it is desirable to obtain solutions that are multi-objectively optimum and robust. A robust solution is one that as a result of input uncertainty has variations in its objective and constraint functions which are within an acceptable range. This paper presents a new approximation-assisted MORO (AA-MORO) technique with interval uncertainty. The technique is a significant improvement, in terms of computational effort, over previously reported MORO techniques. AA-MORO includes an upper-level problem that solves a multi-objective optimization problem whose feasible domain is iteratively restricted by constraint cuts determined by a lower-level optimization problem. AA-MORO also includes an online approximation wherein optimal solutions from the upper- and lower-level optimization problems are used to iteratively improve an approximation to the objective and constraint functions. Several examples are used to test the proposed technique. The test results show that the proposed AA-MORO reasonably approximates solutions obtained from previous MORO approaches while its computational effort, in terms of the number of function calls, is significantly reduced compared to the previous approaches.

scholarly journals New Safe Approximation of Ambiguous Probabilistic Constraints for Financial Optimization Problem

2019 ◽  
Vol 2019 ◽  
pp. 1-19
Author(s):  
NingNing Du ◽  
Yan-Kui Liu ◽  
Ying Liu
Keyword(s):  
Value At Risk ◽  
Optimization Problem ◽  
Optimization Problems ◽  
Random Perturbation ◽  
Probabilistic Constraints ◽  
Optimization Approach ◽  
Chinese Stock Market ◽  
Robust Solution ◽  
Return Rates ◽  
Financial Optimization

In financial optimization problem, the optimal portfolios usually depend heavily on the distributions of uncertain return rates. When the distributional information about uncertain return rates is partially available, it is important for investors to find a robust solution for immunization against the distribution uncertainty. The main contribution of this paper is to develop an ambiguous value-at-risk (VaR) optimization framework for portfolio selection problems, where the distributions of uncertain return rates are partially available. For tractability consideration, we deal with new safe approximations of ambiguous probabilistic constraints under two types of random perturbation sets and obtain two equivalent tractable formulations of the ambiguous probabilistic constraints. Finally, to demonstrate the potential for solving portfolio optimization problems, we provide a practical example about the Chinese stock market. The advantage of the proposed robust optimization method is also illustrated by comparing it with the existing optimization approach via numerical experiments.

A new look at generating multi-join continuous query plans: A qualified plan generation problem

2010 ◽  
Vol 69 (5) ◽  
pp. 424-443 ◽  
Author(s):  
Yali Zhu ◽  
Venkatesh Raghavan ◽  
Elke A. Rundensteiner
Keyword(s):  
Continuous Query ◽  
Plan Generation ◽  
Generation Problem ◽  
New Look

scholarly journals Look Up in the Sky: Latent Content Analysis of the Real Life Superhero Community

2016 ◽  
Author(s):  
Daniel White ◽  
Marianna Szabo ◽  
Niko Tiliopoulos ◽  
Paul Rhodes ◽  
Michael Spurrier ◽  
...  
Keyword(s):  
Content Analysis ◽  
Online Survey ◽  
Academic Research ◽  
Real Life ◽  
Demographic Information ◽  
The Real ◽  
Internet Forums ◽  
High Degree ◽  
Latent Content ◽  
Degree Of Heterogeneity

The Real Life Superhero (RLSH) subculture is a growing global community of individuals who adopt the superhero motif and are motivated by prosocial goals. Although the community has been the focus of documentaries, news articles and numerous internet forums, little academic research has been conducted on the composition of this subculture. Through the use of an online survey, socio-demographic information about this community was collected. This data was compiled and analysed via qualitative means to develop not only an overarching review of the composition of the subculture but also how members perceived themselves and other members. Membership and identity within the community was strongly tied to the activities and focus of each member, predominantly community and crime prevention orientated. The study identified a high degree of heterogeneity within the community with subdivisions focused on the perceptions of legal boundaries, focus of activities and level of authenticity.

scholarly journals A Cross Entropy Algorithm for Classification with $\delta$−Patterns

2006 ◽  
Vol DMTCS Proceedings vol. AG,... (Proceedings) ◽  
Author(s):  
Gabriela Alexe ◽  
Gyan Bhanot ◽  
Adriana Climescu-Haulica
Keyword(s):  
Optimization Problem ◽  
Genomic Data ◽  
Maximal Clique ◽  
Large Datasets ◽  
Entropy Method ◽  
Combinatorial Optimization Problem ◽  
Cross Entropy ◽  
Cross Entropy Method ◽  
International Audience ◽  
Generation Problem

International audience A classification strategy based on $\delta$-patterns is developed via a combinatorial optimization problem related with the maximal clique generation problem on a graph. The proposed solution uses the cross entropy method and has the advantage to be particularly suitable for large datasets. This study is tailored for the particularities of the genomic data.

Robustness of Design Through Minimum Sensitivity

1992 ◽  
Vol 114 (2) ◽  
pp. 213-217 ◽  
Author(s):  
A. D. Belegundu ◽  
Shenghua Zhang
Keyword(s):  
Optimization Problem ◽  
Probability Distributions ◽  
Uncertain Variable ◽  
Design Stage ◽  
Cross Sectional ◽  
Uncertain Variables ◽  
Design Variables ◽  
Variable Reduction ◽  
Minimum Sensitivity ◽  
Selection Of

The problem of designing mechanical systems or components under uncertainty is considered. The basic idea is to ensure quality control at the design stage by minimizing sensitivity of the response to uncertain variables by proper selection of design variables. The formulation does not involve probability distributions. It is proved, however, that when the response is linear in the uncertain variable, reduction in sensitivity implies lesser probability of failure. The proof is generalized to the non-linear case under certain restrictions. In one example, the design of a three-bar truss is considered. The length of one of the bars is considered to be the uncertain variable while cross-sectional areas are the design variables. The sensitivity of the x-displacement is minimized. The constrained optimization problem is solved using a nonlinear programming code. A criterion which can help identify some of the problems where robustness in design is critical is discussed.

Weapon-Target Assignment Problem in the Warship Fleet Based on Fast and Elitist Non-Dominated Sorting Genetic Algorithm

2012 ◽  
Vol 605-607 ◽  
pp. 2399-2404
Author(s):  
Xin Lai Chen ◽  
Song Shen
Keyword(s):  
Genetic Algorithm ◽  
Assignment Problem ◽  
Optimization Problem ◽  
Pareto Solution ◽  
Robust Solution ◽  
Target Assignment

Represent a new Weapon-Target Assignment (WTA) model of warship fleet as to the characteristic of the modern naval battle field and the battle modality. This model considers the WTA to a multi-objects optimization problem, and a Fast and Elitist Non-Dominated Sorting Genetic Algorithm (FENSGA) is applied to resolve this model. The FENSGA can reach a set of wide-distributing, robust solution. One running of the FENSGA can reach a multi-Pareto solution, which the commander can select from. A simulation is given to prove the validity of this model and algorithm.

scholarly journals Minimax Estimation in Regression under Sample Conformity Constraints

Mathematics ◽  
2021 ◽  
Vol 9 (10) ◽  
pp. 1080
Author(s):  
Andrey Borisov
Keyword(s):  
Optimization Problem ◽  
Probability Distributions ◽  
Essential Feature ◽  
Estimation Error ◽  
Saddle Points ◽  
Minimax Problem ◽  
Estimation Of Parameters ◽  
Minimax Optimization ◽  
Optimal Estimator ◽  
Regression Parameters

The paper is devoted to the guaranteeing estimation of parameters in the uncertain stochastic nonlinear regression. The loss function is the conditional mean square of the estimation error given the available observations. The distribution of regression parameters is partially unknown, and the uncertainty is described by a subset of probability distributions with a known compact domain. The essential feature is the usage of some additional constraints describing the conformity of the uncertain distribution to the realized observation sample. The paper contains various examples of the conformity indices. The estimation task is formulated as the minimax optimization problem, which, in turn, is solved in terms of saddle points. The paper presents the characterization of both the optimal estimator and the set of least favorable distributions. The saddle points are found via the solution to a dual finite-dimensional optimization problem, which is simpler than the initial minimax problem. The paper proposes a numerical mesh procedure of the solution to the dual optimization problem. The interconnection between the least favorable distributions under the conformity constraint, and their Pareto efficiency in the sense of a vector criterion is also indicated. The influence of various conformity constraints on the estimation performance is demonstrated by the illustrative numerical examples.

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